Neuromonitoring is a subfield of clinical patient monitoring focused on measuring various aspects of brain function and on changes therein caused by neurological diseases, accidents, and drugs commonly used to induce and maintain anesthesia in an operation room or sedation in patients under critical or intensive care.
Electroencephalography (EEG) is a well-established method for assessing brain activity. When measurement electrodes are attached on the skin of the skull surface, the weak biopotential signals generated in brain cortex may be recorded and analyzed. The EEG has been in wide use for decades in basic research of the neural systems of the brain as well as in the clinical diagnosis of various central nervous system diseases and disorders.
The EEG signal represents the sum of excitatory and inhibitory potentials of large numbers of cortical pyramidal neurons, which are organized in columns. Each EEG electrode senses the average activity of several thousands of cortical pyramidal neurons.
The EEG signal is often divided into four different frequency bands: Delta (0.5-3.5 Hz), Theta (3.5-7.0 Hz), Alpha (7.0-13.0 Hz), and Beta (13.0-32.0 Hz). In an adult, Alpha waves are found during periods of wakefulness, and they may disappear entirely during sleep. Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
Surface EEG always includes various artifacts and confounding signals that hamper the analysis of the brain waves. Eye movements, eye blinks, facial muscle activity, and head movements are well-known sources of interference. During EEG review, these types of artifact may interfere with the detection and analysis of the events of interest. The methods dealing with EEG artifacts may be divided between methods that remove artifacts without considering brain activity and techniques that remove artifact by attempting to separate artifact and brain activities from each other. A straightforward approach is to discard contaminated EEG epochs from further analysis based on one or more electro-oculogram (EOG) signals indicative of ocular activity and thus of the artifact caused by eye movements. This kind of method is disclosed for example in the article Virtanen J, Ahveninen J, Ilmoniemi R J, Näätänen R and Pekkonen E: Replicability of MEG and EEG measures of the auditory N1/N1m-responses, Electroencephalography and clinical Neurophysiology, 108, 291-298, 1998. This is usually the method of choice in recordings with relatively small number of EEG channels.
Another well-known approach is the EOG subtraction method, in which the proportion of ocular contamination is estimated for each EEG channel. To obtain corrected EEG data, the EOG signals measured are scaled by the estimated proportion and the scaled EOG signals are subtracted from the original EEG signals. However, as the EOG is not only sensitive to eye artifacts but also contains brain activity, this method may render the relevant brain signals distorted.
In brain research, a large number of EEG channels may be used by placing, respectively, a large number of electrodes over multiple areas of the scalp to obtain a mapping of the potential distribution over the scalp. In these applications, the additional degrees of freedom provided by the large number of EEG channels allow the use of more sophisticated methods of EOG artifact removal.
Several methods that differ in the way how brain and artifact activity are separated have been proposed. One known method is the Independent Component Analysis (ICA), which assumes, for example, that the summation of potentials arising from different parts of the brain, scalp, and body is linear at the electrodes. ICA-based artifact correction thus removes and separates artifacts by linear decomposition.
However, the great number of channels/electrodes needed render the methods used in brain research inappropriate for such clinical applications, in which the number of EEG signals/channels is to be kept, due to practical reasons, much lower, typically in one or two. In many clinical applications it is advantageous to place the EEG measurement electrodes only onto the forehead or other hairless areas of the patient's head, while artifact is removed by rejecting contaminated EEG epochs based on one or more EOG channels measured separately. Alternatively, artifact may be removed without the use of EOG channels based on the properties of the EEG signal itself, for example by rejecting epochs including excessive amplitudes of the signal. Rejected epochs may optionally be replaced by new data points derived from non-rejected data points by interpolation, for example.
A drawback of the EOG-based clinical methods is that efficient detection of the contaminated EEG epochs requires separate electrodes for recording the EOG signal(s). If no separate EOG electrodes are used in clinical applications, the artifact removal remains inefficient, since the omission of the EOG electrodes makes the knowledge about the presence of artifact unreliable. EOG is present and often visible in any facial electrode pair. These same electrode pairs also pick up low frequency brain activity. In order to obtain as independent information as possible about eye movements, dedicated electrodes are attached around the eyes. However, attaching the electrodes adds to the work of the nursing staff and causes inconvenience for the patient.
Movement of the electrodes relative to the skin is another potential source of artifacts. The relative movement may be caused by spontaneous head movements or head movements due to mechanical ventilation, for example. Vibration caused by the nursing staff walking close to the patient or accidentally rocking the patient bed may also couple to the electrode lead wires. Apart from the measurement of eye movements or blinks, other measurements of skin surface potential do not provide independent information about the existence of movement artifacts. Head movements can be monitored using, for example, an acceleration transducer. This method, however, has two drawbacks. First, it is not clear how the head movements and the EEG artifacts are related, because the amplitude of the possible EEG deflections depend on multiple variables, such as the quality of the electrode contact, the direction of the head movement, possible tension in the electrode lead wires, etc. Second, the method requires a dedicated acceleration transducer component either attached separately on the skin of the patient or integrated as part of one of the electrodes. This translates to additional cost and increased complexity of the system and its use.
Facial muscle activity causes high frequency (30-150 Hz) action potential signals (EMG) to superimpose on the EEG. In addition, the facial muscle activity causes low frequency components to the signal due to the movement of the electrodes relative to the skin. However, predicting low frequency EEG artifacts based on the high frequency signal content is not reliable, because muscle activity does not necessarily imply electrode movement and thus EEG artifact.
The present invention seeks to alleviate or eliminate the above-mentioned drawbacks and to accomplish an uncomplicated artifact detection mechanism suitable for clinical use.